As semiconductor and nanotechnology produces smaller and smaller circuits and other elements, it becomes more difficult to produce and manipulate the nanometer scale objects. While it is known that it is desirable to automate nano-processes, the difficult in measuring those processes makes automation difficult, if not impossible for many processes.
Analysis and measurement of samples is typically performed with a sample lamella cut from a larger semiconductor device with an ion beam, such as the process depicted in FIG. 1. As shown, an ion beam column 102 is used to project an ion beam 104 to cut a sample lamella 110 from a larger semiconductor device 108, moving the sample device with a tilt stage 106. A protective layer may be deposited over the region of interest inside the lamella 110 to protect it from ion implantation. The workpiece lamella must then the thinned to a precise thickness with the desired region of interest, containing features of interest to be examined, placed at a desired location within the finished lamella.
The thinning process is made difficult because the line widths of features achievable in semiconductor processing are significantly less than 100 nm. To control critical processes during fabrication, it is necessary to observe and measure the results of those processes. Scanning electron microscopes (SEM) are used to observe microscopic features, but as the feature size of fabricated structures decreases, the resolution of SEM is insufficient, and it is necessary to observe defects on a transmission electron microscope (TEM). While a SEM can observe a feature on or near the surface of a thick work piece, to observe a sample on a TEM, it needs to be thinned to less than 100 nm so that electrons will travel through the sample. It can be exceedingly difficult to thin a sample to less than 100 nm while ensuring that the feature that requires observation remains in the sample and is not milled away in the thinning process.
An operator will typically observe the sample as it is being thinned, stopping regularly to observe the sample to see whether or not the feature is exposed for observation. When the feature to be observed is exposed on the surface, then thinning is stopped. This process is known as end-pointing, and can be very time consuming and labor intensive. Moreover, the results are inconsistent from operator to operator because the decision of when to stop is subjective.
U.S. Pat. Pub. 2010243889 of Farber et al. describes a method of end-pointing when forming a lamella for viewing on a transmission electron microscope. In accordance with Farber, secondary particles are collected as the ion beam thins the lamella, and the image formed from the secondary particles is used to form a rough image of the cross section. The image is rough because the ion beam impacts the lamella at a glancing angle, and because the secondary particles from deep in the trench are not detected as well as the secondary particles from higher up in the trench.
There are other known methods of end-pointing a lamella being prepared for viewing on a transmission electron microscope. A process for forming a lamella is described, for example, in U.S. Publication No. 2016/0126060, filed May 21, 2012, for “Preparation of Lamellae for TEM Viewing” which is assigned to the assignee of the present invention and is hereby incorporated by reference. In that process, thinning is performed by the ion beam, using either a fiducial or an edge of the lamella itself as a reference to determine the placement of the beam for the final cut. This beam placement is not sufficiently accurate in some cases to stop the milling at the desired location, which may be determined by the exposure of a feature in the face of the cross section, rather than by a thickness of the cross section. The process uses SEM images and performs image recognition software for gross determination of the end-point, and then uses a dimension determined using edge recognition for fine end-pointing. Edge recognition is simpler than image recognition and looks at the contrast of the pixels in an image and determines an edge by a change in contrast. A smoothing function is typically applied to produce a smooth curve corresponding to the edge. After edges are recognized, geometric relationships between the edges, such as distances or angles, are determined and used to evaluate the image to determine when to stop milling. The process provides a closed loop feedback, in which after, one or more fine mill steps, the dimension is checked to determine whether or not to cease milling.
In these known processes, the repeated sample re-positioning and the relatively low signal provided by low-kV SEM images make the end-pointing very slow. Most processes also requires an expert dual-beam operator. Especially for very small features such as the features of state-of-the-art FinFET transistor devices, a very thin lamella of approximately 20 nm thickness is required to isolate the feature of interest for TEM examination. Many repeated steps are often required to produce such a lamella, including thinning the workpiece lamella in very small steps and frequently flipping the workpiece lamella to thin the opposite side. Often, the lamella is required to be removed and placed in a TEM device for a high resolution scan in order to make decisions for the endpointing process, such as which side of the workpiece lamella to mill and how thick of a layer to mill away.
What is needed are ways to improve the endpointing process that speed up the process. What is also needed are ways to perform endpointing that do not require scans in a separate TEM device. Also, ways to further automate the end-pointing process are needed. Finally, processes that can be performed with less skilled operators are needed due to the high cost of using highly skilled operators for these long and tedious processes.